Production throughput is an important performance indicator in an oil and/or a gas production system. Herein, production throughput is intended to mean the oil and/or water and/or gas production per time interval. The throughput depends on many different factors; some may be specific to each production system, others are more general. One important general factor is how the limited processing capacity of the production system is utilized.
Manipulated variables in the production system may be settings related to routing, production valves or gas lift valves, and these manipulated variables should be chosen such that throughput is maximized while processing capacities are not over-utilized.
By using computer simulation or a mathematical optimization method, good values of manipulated variables may be found. The accuracy of the computer simulations and mathematical optimization methods depend on the accuracy of the parameters used in their mathematical model. To ensure good accuracy of the simulation, parameters of the mathematical model are normally determined through experiments.
Experiments on wells are generally performed by routing an individual well to a dedicated test separator. The oil, water and/or gas rates at the outlet of the separator are then measured. A test may take several hours, constraining the frequency at which wells can be tested. Oftentimes, production is only measured for a single value of manipulated variables; production may for instance be measured for the current production valve opening, a single-rate well test. During a multi-rate well test, multiple values of manipulated variables are tested for a single well, for instance by measuring production for multiple production valve openings. As production may require a time to settle once manipulated variables are changed, multi-rate well tests require a well to be routed to a test separator for a longer duration and may therefore be more costly. As the production system may already be producing at the constraints of its processing capacities, care must be taken not to exceed these constraints when a multi-rate well test is performed, so that multi-rate well tests have a risk and a possible cost related to temporary reduction in throughput.
For an oil and/or a gas system a well test is typically performed by routing the production from a single well (one of several wells in the system), 105, 106 or 127 (see FIG. 1) to a dedicated test separator 107. This allows for measuring of parameters related to this single (specific) well. The values measured are typically the flow rates of oil, water and gas, as well as test separator pressure and/or temperature, up- and downstream wellhead pressure and temperature, and choke opening. There is typically only one or a few such test separator(s) 107 in each production system. Therefore, all wells in the system cannot continuously be monitored.
The currently used method for predicting throughput in oil and/or gas production system as manipulated variables are changed is to use commercial simulators, see for instance Wang (2003) “Development and application of production optimization for petroleum fields”. To increase simulator accuracy, commercial simulators are often fitted to single- and/or multi-rate well tests. Often, predictions of production found by commercial simulators for varying values of manipulated variables are collected in tables, so called proxy-models, and these proxy-models are fitted to production data rather than fitting commercial simulators directly, see for instance Zangl, G., Graf, T. and Al-Kinani, A. (2006) “Proxy modeling in production optimization” in ‘SPE Europec/EAGE Annual Conference and Exhibition’ (SPE 100131).
A drawback of predictions based on commercial simulators either directly or through proxy-models, is that some error in the ability of the model to predict responses in production to changes in manipulated variables must be expected due to the complexity of modeling multiphase flows. This shortcoming could be mitigated by regularly performing multi-rate well tests for all wells and fitting models to these tests. The cost in terms of increased risk for complete or partial shut downs and/or temporarily reduced throughput of performing multi-rate well tests means that this is usually not a viable option. Fitting models to single-rate well tests may also improve model predictions somewhat, but single-rate well tests do not reveal information on the response of production to change in manipulated variables.
An international patent application WO2006/048418 entitled “Method and system for production metering of oil wells”, discloses a method and system for the metering of oil wells. According to the method a series of well tests is performed initially, during which manipulated variables of a tested well are varied and measured variation in production is used to determine a dynamic fingerprint. During the series of well tests, the manipulated variables of one well are varied, while the production from other wells is maintained substantially constant or interrupted. Weights are found iteratively so that measured total production rates are close to a weighted sum of dynamic fingerprints. The resulting metering system is intended for determining current production given current production measurements.
Methods for fitting reservoir models to production data, usually from normal operation, are referred to as history matching. Costa, A., Schiozer, D. and Poletto, C. (2006), “Use of uncertainty analysis to improve production history matching and the decision-making process” in ‘SPE Europec/EAGE Annual Conference and Exhibition’(SPE 99324), discusses how geological uncertainties can be quantified in history matching to determine the risk curve when predicting future reservoir performance. The authors suggested defining uncertainties in geological parameters from experience, before fitting models to historical production data for a variety of different values of uncertainty geological parameters and simulating with each model to determine the significance of said uncertainty on predictions of future production. Diab, A., Griess, B. and Shulze-Riegert, R. (2006), “Application of global optimization techniques for model validation and prediction scenarios of a north African oil field” in ‘SPE Europec/EAGE Annual Conference and Exhibition’ (SPE 1001093) discusses the use of the Multipurpose Environment for Parallel Optimization (MEPO). The authors discuss a scheme to perform multiple history matches using different optimization algorithms to obtain multiple estimates of model parameters in dynamic reservoir models.
Reservoir models are designed to describe the geological properties and states of a reservoir, emphasis is not on describing oil and/or gas production system and the relationship between production and manipulated variables. Reservoir models are used to determine strategies for injection of water and/or gas at injection wells in the reservoir, for aiding decisions related to field development such as installing new equipment or wells, or for making predictions on future trends in production on the timescale of months and years. Reservoir models are often large and complex and may require days to solve and are labor-intensive to maintain due to their complexity.
On the timescale of hours and days, the effects of change in geological properties and states or responses to change in injection will be barely noticeable on throughput in an oil and/or a gas production system. Throughput can be varied in a matter of minutes by altering manipulated variables related to gas lift, production valve or routing settings. Reservoir models will not be suited for maximizing throughput on these timescales, as any conclusions found by simulating reservoir models may be outdated by the time simulations are complete, and due to their complexity it may be difficult for reservoir models to predict changes in throughput correctly as manipulated variables change.
Performing a series of well tests may have a cost, as it may mean that throughput is not at its maximum during the tests. Instead of exploiting a series of well tests, it is conceivable to exploit historical measurements of normal operations, the difficulty of this alternate approach is that some manipulated variables may have been varied simultaneously and others may have been varied little or not at all. The quality of predicted throughput derived from historical measurements of production during normal operations may thus vary.